Abstract

Single-wavelength high performance liquid chromatography-diode-array-detector (HPLC-DAD) and multi-wavelength combined HPLC methods were researched and developed in order to compare their performance for the classification of complex substances. Thus, the aims of this work were: to compare the performance of the single- and multi-wavelength HPLC-DAD methods for analysis of complex substances – in this context, the Radix Paeoniae herbs, which were classified on the basis of their component species and geographical origin. Three classification methods, Linear discriminant analysis (LDA), Radial basis function artificial neural network (RBF-ANN) and Least squares-support vector machine (LS-SVM), were compared on the basis of their performance in discriminating the Radix Paeoniae samples. The results showed that the multi-wavelength data produced better classification results of the two HPLC methods. This was so, irrespective of the chemometrics method used. However, the LS-SVM models were significantly better in classifying the herb samples. Consequently, the multi-wavelength HPLC-DAD approach is a strong alternative to the more common single-wavelength method, and the LS-SVM was the method of choice for classification of the complex substances such as the Radix Paeoniae herbs.